Synopsis
Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Modeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets, including proper Gaussian Markov random fields, dynamic multiscale spatio-temporal models, and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners, and to stimulate additional research in these important areas of spatial and spatio-temporal statistics.
Key topics:
- Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models.
- Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection.
- Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects.
- Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations.
- Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression.
- Multiscale spatio-temporal assimilation of computer model output and monitoring station data.
- Dynamic multiscale heteroscedastic multivariate spatio-temporal models.
- The M-open multiple optima paradox and some of its practical implications for multiscale modeling.
- Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes.
The audience for this book are practitioners, researchers, and graduate students in statistics, data science, machine learning, and related fields. Prerequisites for this book are master's-level courses on statistical inference, linear models, and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics, as well as supplementary material for graduate courses on spatial and spatio-temporal modeling.
"synopsis" may belong to another edition of this title.
About the Author
Marco A. R. Ferreira is a Professor in the Department of Statistics at Virginia Tech. Marco has served the statistics profession in editorial boards of multiple scientific journals including the journal Bayesian Analysis, in several committees of the International Society for Bayesian Analysis and the American Statistical Association, as well as in scientific committees of numerous domestic and international conferences. Marco's current research areas include dynamic models for time series and spatiotemporal data, multiscale models, objective Bayesian methods, stochastic search algorithms, and statistical computation. Major areas of application include bioinformatics, economics, epidemiology, and environmental science. Marco's research has been funded by grants from industry, the National Science Foundation, and the National Institute of Health. Marco has published important scientific papers in top journals such as the Journal of the American Statistical Association, the Journal of the Royal Statistical Society, Biometrika, and Bayesian Analysis. At the time of this writing, Marco has advised over 15 Ph.D. students and postdocs who work in academic, industrial, and governmental positions.
"About this title" may belong to another edition of this title.
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Hardcover. Condition: new. Hardcover. Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Modeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets, including proper Gaussian Markov random fields, dynamic multiscale spatio-temporal models, and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners, and to stimulate additional research in these important areas of spatial and spatio-temporal statistics.Key topics:Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models.Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection.Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects.Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations.Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression.Multiscale spatio-temporal assimilation of computer model output and monitoring station data.Dynamic multiscale heteroscedastic multivariate spatio-temporal models.The M-open multiple optima paradox and some of its practical implications for multiscale modeling.Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes.The audience for this book are practitioners, researchers, and graduate students in statistics, data science, machine learning, and related fields. Prerequisites for this book are master's-level courses on statistical inference, linear models, and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics, as well as supplementary material for graduate courses on spatial and spatio-temporal modeling. Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Aims to fill some of this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032622095
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